Choi, Jae-YoungPrasad, RachitChoi, Seongim2024-10-012024-10-012024-09-03Choi, J.-Y.; Prasad, R.; Choi, S. Towards Autonomous Operation of UAVs Using Data-Driven Target Tracking and Dynamic, Distributed Path Planning Methods. Aerospace 2024, 11, 720.https://hdl.handle.net/10919/121239A hybrid real-time path planning method has been developed that employs data-driven target UAV trajectory tracking methods. It aims to autonomously manage the distributed operation of multiple UAVs in dynamically changing environments. The target tracking methods include a Gaussian mixture model, a long short-term memory network, and extended Kalman filters with pre-specified motion models. Real-time vehicle-to-vehicle communication is assumed through a cloud-based system, enabling virtual, dynamic local networks to facilitate the high demand of vehicles in airspace. The method generates optimal paths by adaptively employing the dynamic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>A</mi><mo>*</mo></msup></semantics></math></inline-formula> algorithm and the artificial potential field method, with minimum snap trajectory smoothing to enhance path trackability during real flights. For validation, software-in-the-loop testing is performed in a dynamic environment composed of multiple quadrotors. The results demonstrate the framework&rsquo;s ability to generate real-time, collision-free flight paths at low computational costs.application/pdfenCreative Commons Attribution 4.0 InternationalUTMUAS dynamicdistributed path planningdata-driven target trackingsoftware-in-the-loopArticle - Refereed2024-09-27https://doi.org/10.3390/aerospace11090720